Implementing High Dimensional Reduction Analysis on Histocytometric Data.

In a previous protocol article, we demonstrated construction of a histocytometry pipeline that is capable of both segmenting highly aggregated cell populations and retaining the original intensity data range of the input microscopy images. In the protocol presented here, using the output from the aforementioned article, we demonstrate how to phenotype the data using the high dimensional reduction analysis technique optimized t-distributed stochastic neighbor embedding (opt-t-SNE) and compare it to traditional manual gating. Additionally, we present a protocol illustrating the advantage of the inclusion of cell junction/membrane markers for accurately segmenting highly aggregated cell populations in ilastik. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Phenotyping lymph node populations using manual gating Basic Protocol 2: Phenotyping lymph node populations using t-SNE dimensional reduction Support Protocol: ilastik segmentation using a pan marker.

Creation of High-Dimensional Reduction Analysis-Compatible Histocytometry Files from Images of Densely-Packed Cells and/or Variable Stain Intensity.

The power of high-dimensional reduction techniques using multiparameter images has been demonstrated across a variety of different publications. Recently, we published an end-to-end low-cost GUI-based protocol for performing histocytometric spatial analysis on images derived from the most common microscope image formats. However, this protocol is limited by the normalized marker intensity outputs and the difficulty in processing images of highly aggregated and/or exceptionally heterogenous cell populations. Here we present the basic protocols required to construct an advanced histocytometric data file using only freeware. This data file is compatible with images containing cell nuclei clusters that are difficult to segment, and results in histocytometry files retaining the original marker intensity values of the microscopic images they were derived from. This is especially useful in cells that are phenotyped based on relative marker expression levels. Histocytometry data files produced by these protocols are compatible with high-dimensional reduction analysis using marker intensity data, such as tSNEs. This methodology is showcased using stitched microscopic images of murine lymph nodes, complex organs with highly aggregated heterogenous cell populations, that are typically difficult to segment. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Image preprocessing and generation of nuclei marker probability maps Basic Protocol 2: Cell segmentation using ilastik-derived probability maps Basic Protocol 3: Generation of histocytometric .fcs files.

Peripheral B-cell dysregulation is associated with relapse after long-term quiescence in patients with multiple sclerosis.

B cells play a major role in multiple sclerosis (MS), with many successful therapeutics capable of removing them from circulation. One such therapy, alemtuzumab, is thought to reset the immune system without the need for ongoing therapy in a proportion of patients. The exact cells contributing to disease pathogenesis and quiescence remain to be identified. We utilized mass cytometry to analyze B cells from the blood of patients with relapse-remitting MS (RRMS) before and after alemtuzumab treatment, and during relapse. A complementary RRMS cohort was analyzed by single-cell RNA sequencing. The R package "Spectre" was used to analyze these data, incorporating FlowSOM clustering, sparse partial least squares-discriminant analysis and permutational multivariate analysis of variance. Immunoglobulin (Ig)A+ and IgG1 + B-cell numbers were altered, including higher IgG1 + B cells during relapse. B-cell linker protein (BLNK), CD40 and CD210 expression by B cells was lower in patients with RRMS compared with non-MS controls, with similar results at the transcriptomic level. Finally, alemtuzumab restored BLNK, CD40 and CD210 expression by IgA+ and IgG1 + B cells, which was altered again during relapse. These data suggest that impairment of IgA+ and IgG1 + B cells may contribute to MS pathogenesis, which can be restored by alemtuzumab.

How to Build an Image-Processing Pipeline for Automating Multiparameter Histocytometry Analysis.

Until relatively recently, analysis of imaging data has been primarily quantitative and limited to 3-4 markers. The advancement of various technologies overcoming this marker limitation provided the capability of analyzing multiparameter imaging data down to the single cell level, termed histocytometry. Currently, most published end-to-end histocytometric analysis of imaging data is performed using expensive commercial programs or freely available analysis packages that require significant knowledge of programming languages for execution. Here we present a protocol that performs cell segmentation, phenotyping and spatial analysis, using software with easy-to-use GUIs (graphical user interfaces). These protocols allow the user to derive spatial and phenotypical data for the analysis of multiparameter microscopic images from most imaging platforms in a low-cost manner. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Cell Segmentation and generation of histocytometric .csv file Basic Protocol 2: Phenotyping of cell populations Basic Protocol 3: Spatial relationship analyses of phenotyped populations Support Protocol 1: Nuclei Segmentation Accuracy Test Support Protocol 2: Correcting y-axis Inversion of Histocytometry Data Relative to Original Image File.

Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre.

As the size and complexity of high-dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end-to-end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

T lymphocyte and monocyte subsets are dysregulated in type 1 diabetes patients with peripheral neuropathic pain.

Diabetic neuropathic pain is a common and devastating complication of type 1 diabetes, but the mechanism by which it develops and persists is yet to be fully elucidated. This study utilised high-dimensional suspension mass cytometry in a pilot cohort to investigate differences in peripheral blood immunophenotypes between type 1 diabetes patients with (n â€‹= â€‹9) and without (n â€‹= â€‹9) peripheral neuropathic pain. The abundance and activation of several leukocyte subsets were investigated with unsupervised clustering approaches FlowSOM and SPADE, as well as by manual gating. Major findings included a proportional increase in CD4+ central memory T cells and an absolute increase in classical monocytes, non-classical monocytes, and mature natural killer cells in type 1 diabetes patients with pain compared to those without pain. The expression of CD27, CD127, and CD39 was upregulated on select T cell populations, and the phosphorylated form of pro-inflammatory transcription factor MK2 was upregulated across most populations. These results provide evidence that distinct immunological signatures are associated with painful neuropathy in type 1 diabetes patients. Further research may link these changes to mechanisms by which pain in type 1 diabetes is initiated and maintained, paving the way for much needed targeted treatments.

Biosafety during a pandemic: shared resource laboratories rise to the challenge.

Biosafety has always been an important aspect of daily work in any research institution, particularly for cytometry Shared Resources Laboratories (SRLs). SRLs are common-use spaces that facilitate the sharing of knowledge, expertise, and ideas. This sharing inescapably involves contact and interaction of all those within this working environment on a daily basis. The current pandemic caused by SARS-CoV-2 has prompted the re-evaluation of many policies governing the operations of SRLs. Here we identify and review the unique challenges SRLs face in maintaining biosafety standards, highlighting the potential risks associated with not only cytometry instrumentation and samples, but also the people working with them. We propose possible solutions to safety issues raised by the COVID-19 pandemic and provide tools for facilities to adapt to evolving guidelines and future challenges.

Mass Cytometry Reveals a Sustained Reduction in CD16+ Natural Killer Cells Following Chemotherapy in Colorectal Cancer Patients.

The immune system and inflammation plays a significant role in tumour immune evasion enhancing disease progression and reducing survival in colorectal cancer (CRC). Patients with advanced stages of colorectal cancer will all undergo treatment with cytotoxic chemotherapy which may alter the complexity of immune cell populations. This study used mass cytometry to investigate the circulating immune cell profile of advanced CRC patients following acute and chronic doses of standard cytotoxic chemotherapy and analysed seven major immune cell populations and over 20 subpopulations. Unsupervised clustering analysis of the mass cytometry data revealed a decrease in NK cells following one cycle of cytotoxic chemotherapy. Investigation into the NK sub-population revealed a decline in the CD56dim CD16+ NK cell population following acute and chronic chemotherapy treatment. Further analysis into the frequency of the NK cell sub-populations during the long-term chemotherapy treatment revealed a shift in the sub-populations, with a decrease in the mature, cytotoxic CD56dim CD16+ accompanied by a significant increase in the less mature CD56dim CD16- and CD56bright NK cell populations. Furthermore, analysis of the phosphorylation status of signalling responses in the NK cells found significant differences in pERK, pP38, pSTAT3, and pSTAT5 between the patients and healthy volunteers and remained unchanged throughout the chemotherapy. Results from this study reveals that there is a sustained decrease in the mature CD16+ NK cell sub-population frequency following long-term chemotherapy which may have clinical implications in therapeutic decision making.

Staining of Phosphorylated Signalling Markers Protocol for Mass Cytometry.

Mass cytometry is a multi-parametric technique that offers insight into functional and biological systems at a single cell level (Tanner et al., Cancer Immunol Immunother 62:955-965, 2013). One of the major advantages of mass cytometry is the ability to measure multiple intracellular markers, including phosphorylated proteins that are part of major signaling pathways, such as NF-κB, JAK/STAT, and ERK/MAPK. Here we describe an optimized mass cytometry protocol for staining human clinical blood samples with panels that include phosphorylated antibodies.

Cancer-Related Systemic Inflammation: The Challenges and Therapeutic Opportunities for Personalized Medicine.

Over the last decade there has been significant progress towards the development of personalized or "precision" medicine for many patients with cancer. However, there still remain subpopulations of cancer patients that do not possess a tumor mutation profile that is successfully targeted by the newer molecular anticancer drugs and further personalized approaches are needed. The presence of cancer-related systemic inflammation represents an underappreciated subpopulation of cancer patients needing personalized therapy. For ∼25% of all advanced cancer patients, regardless of histological subtype, the patients with systemic inflammation have significantly poorer response to chemotherapy and also shorter overall survival compared to those cancer patients without inflammation. The development of cancer-related systemic inflammation involves interactions between host and tumor cells that are potential new drug targets in cancer chemotherapy. In this review we discuss the challenges and clinical opportunities to develop new therapeutic strategies for this underappreciated drug target.